1. Field of the Invention
The present invention generally relates to system analysis and design in a complex business environment characterized by a set of tightly linked business processes, such as a supply chain, and, more particularly, to a method and tool set to capture the world view embedded in each sub-system in an unambiguous language and to verify consistency along process flows.
2. Background Description
Current software development tool sets for modeling of business processes focus on capturing the flow of data in and out of each task (data flow), and the sequencing of tasks (work flow/process flow). A wide range of approaches, constructs, and tools for modeling processes at this level are available. The common objective is to capture all the data and process flow details needed to enable application software development. Details can be found in software engineering books (e.g., Craig Larman, Applying UML and Patterns, Prentice-Hall, 1998). Software engineering tools (e.g., Rational Rose, Visio) are also available.
Since most businesses have fragmented information systems and little basic support for business processes, this approach to software development has yielded significant benefit in enabling efficient capture of information for code development, and basic coordination in terms of data flow and sequence of work flow. However, ensuring information flow and the sequence of workflow is not sufficient for coordination of the decision making process. Coordination of the decision making at each node or activity point in the process flow happens only when there is a common understanding of the problem. A common understanding of the problem means a consistent view of the objectives, the constraints, and assumptions on data at each point. This becomes an increasingly important issue when considering the evolution of software in enterprises.
The basic infrastructure for coordinated workflow and data flow is now available through the use of enterprise resource planning (ERP) systems, such as offerings from SAP AG, workflow software, such as IBM MQSeries Workflow, or basic technology components for software integration such as Orbix from Iona Technology or IBM MQSeries. Enterprise resource planning systems typically have many modules covering different functions within a business or a supply chain and have built-in coordination or workflow functionality. Workflow software packages are those designed to be controllers that invoke other applications based on a user-specified business process. Basic technology components provides a framework and low level utility functions to allow different applications or software modules communicate (synchronously or asynchronously). Users can use these components for building customized application bridges.
Software systems have advanced from automating transaction processing to enabling decision support. As more tactical decision making is automated, a certain “world view” relevant for the decision becomes encoded in the software. Each world view involves an abstraction and understanding of the input (input data flows for the process), a representation of the objectives, and the operating constraints and underlying assumptions. In addition, algorithmic knowledge to make decisions that best satisfy objectives also becomes encoded in software at each step of the process. Since a variety of software systems are used in each process, the coordination of these systems becomes essential. The first step in such coordination is to capture the world view embedded in each sub-system in an unambiguous language. The second step is to verify consistency along process flows.
A wide range of literature in the area of systems modeling, decision support systems, operations research, artificial intelligence, and software engineering was reviewed.
First, we consider the field of operations research which deals with quantitative modeling of business problems. A complex business environment characterized by a set of related business processes such as a supply chain can often be modeled quantitatively by a large scale mathematical program consisting of an objective and a set of constraints. Mathematical programming is a very powerful and well researched approach to problem solving. Several companies provide tools that are based on math programming solvers, and a modeling language for problem representation as a front-end. Examples are AMPL (Robert Fourer, David M. Gay, Brian W. Kernighan, AMPL: A Modeling Language for Math Programming Package, Brooks/Cole, 1999) and GAMS (Anthony Brooke, David Kendrick, Alexander Meerhaus, Release 2.25 GAMS: A User's Guide, Scientific Press, 1992). A typical mathematical programming language is an algebraic modeling language that is easy for the user to specify objectives and constraints in a form that the solver can understand. However, this is also the problem—the constructs of the language are limited to what the solver can use, and the focus of the modeling is on the specialized application of mathematical optimization which is but an isolated step in the overall business process. Thus, we have languages that specify objectives and constraints as deterministic variables (the randomness of variables cannot be specified) for the purpose of a mathematical programming solver. In “General-Purpose Modeling Languages for Combinational Optimization” by R. Fourer, AMPOD '98, Limassol, Cyprus, Mar. 11, 1998, an extension of an optimization modeling language to include additional operators, and subsequently solution techniques or solvers that can handle such operators is described. Even then we have languages that allow a restricted set of characterizations of randomness that are acceptable to the stochastic solvers they are coupled with. For our proposed tool, we needed a language that is not restricted by the solution method, and general enough to capture problem understanding in a manner suitable for consistency checking.
Computer simulation languages on the other hand provide a more general set of constructs similar to most computer programming languages. Building a model with these languages entails a complete specification of process, data, and algorithmic logic. Again, a variety of languages from different vendors are available, some specialized for certain applications, others more general in nature. SIMAN (C. Dennis Pegden, Robert E. Shannon, and Randy P. Sadowski, Introduction to Simulation Using SIMAN, 2nd edition, McGraw-Hill, 1995) and GPSS (Thomas J. Schriber, An Introduction to Simulation Using GPSS/H, 2nd edition, John Wiley, 1991) are examples of general purpose simulation languages for any discrete event systems, while PROMODEL (Charles Harrell, Birman K. Ghosh, Royce Bowden, Simulation Using promodel, McGraw-Hill, 2000) is an example of a more specialized language for manufacturing system applications. From our perspective, we are not interested in modeling the evolution of a physical process over time, but are interested in the representation of the understanding of a business problem implied by a software system.
Systems modeling languages such as petri-nets also seek to provide system specifications in a manner amenable to analysis of the process flows. Other discrete event system modeling languages and techniques for their analysis are described in Modeling and Control of Logical Discrete Event Systems by Ratnesh Kumar and Vijay K. Garg, Kluwer Academic Publishers, 1995. Similar to computer simulation languages, these systems modeling languages are designed to model the dynamic behavior of a discrete event system over time. They also provide the capability for the user to analyze the system in aspects other than performance evaluation, such as deadlock prevention or automatic control. Again, the invention seeks to complement such modeling tools/languages by adding the missing piece on the flow of understanding in a set of software systems.
Software engineering is an area that offers a variety of languages and tools for modeling process and data flows and constructing software systems. UML (Unified Modeling Language) is an example of a language for specifying, visualizing and constructing the artifacts of software systems. It is a notational system aimed at modeling systems using object oriented concepts. Details about this and other approaches can be found in Craig Larman, supra. These languages are designed to capture the detailed mechanics of a software system, such as the operational relationship between different program objects, assuming that the user already has an understanding of what the system is supposed to do. The invention focuses on specifying what the software system has to do and how to perform it algorithmically or mathematically.
U.S. Pat. No. 5,167,012 to Hayes et al. for “Method for Performing Consistency Checks” is directed to a method for examining a previously consistent state of a rule based expert system after a change is made to a rule or a variable. This is primarily to ensure logical consistency in rule based expert systems whereas our proposed invention deals with process or data flow consistency in business processes.
U.S. Pat. No. 5,446,830 to Crawford, Jr. et al. for “Efficient Non-Monotonic Reasoning Apparatus and Methods” describes a method employed in reasoning systems to approximately determine whether a given fact about one or more objects is consistent with the knowledge base. Again, this is a very specific method employed in any general reasoning system, while our invention is a framework and method for business processes. A technique such as that described in the above patent may or may not find applicability as a way to accomplish a subset of the activities within our proposed method.
U.S. Pat. No. 5,845,258 to Kennedy for “Strategy Driven Planning System and Method of Operation” uses a plan defining a scheduled operation of a user, and a link from strategy to plan. Automatic strategy selection and a planning engine that compares plan to strategy are the focus of this invention. No links with business process flow, data flow, or issues of consistency in flow within a process are dealt with.
U.S. Pat. No. 5,587,935 to Brooks et al. for “Integrated Software Development System Including Group Decision Support Subsystem, Application Subsystem, and Bridge Subsystem Therebetween” discloses an integrated system for creating a process model and writing software based on the process model. The group decision support module is used to create and order the process model according to a protocol. The application development module is used for writing software based on the output of the previous module, and the bridge converts the output of the group decision support subsystem to compatible input for the application development subsystem. The decision support module referred to in this patent is defined to comprise means for exchanging ideas, comments, voting means, etc. to facilitate better software development. Our invention is different in that it deals with process specific decision making, and software that encodes business decision making knowledge; i.e., not decision support for software development, but operational decision making for running a business. Such operational decision making is encoded in software modules as described in previous sections. Our invention can therefore complement the methods described in this disclosure.
U.S. Pat. No. 5,490,097 Swenson et al. for “System and Method for Modeling, Analyzing, and Executing Work Process Plans” defines a method to model work processes, tasks and their sequential or other relationships with each other. It includes methods for tracking the progress and completion of activities. It is used for process modeling that forms an input to our tool.
U.S. Pat. No. 4,658,370 Erman et al. for “Knowledge Engineering Tool” is a tool for building and interpreting a knowledge base. It has methods for storing knowledge and an inference engine. This is a variation of a general purpose expert system.
U.S. Pat. No. 5,233,513 to Doyle for “Business Modeling, Software Engineering and Prototyping Method and Apparatus” describes a program that is useful for creating a business model by analysis of process, data, control, and support. Also, it generates application programs by expert system manipulation of data defining the business model. This is a tool for generating application software and is complementary to our proposed invention. For example, the application software generated can be the subject of analysis, i.e., input, of our invention.